vehicle routing & scheduling: developments & applications in urban distribution assoc. prof....
TRANSCRIPT
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Vehicle Routing & Scheduling: Developments & Applications in Urban Distribution
Assoc. Prof. Russell G. Thompson
Department of Infrastructure [email protected]
IIT Bombay 10th April 2014
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Outline
Vehicle Routing and SchedulingCollaborative Freight SystemsB2CPerformance Based StandardsOngoing ResearchReferences
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Vehicle Routing & Scheduling
Vehicle Routing Problem with Time Windows (VRPTW)
• Decision variables– schedules (trucks to customers)– routes (customer visiting order)
• Objective function: min. operating costs– Travel costs (time & distance)– Penalties (time windows)
• Constraints– Vehicle capacities– Customer time windows
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Time windows and penalties
PenaltyCost ($)
i
e iArrivalTim e
i
l i
1
1
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3rd Customer
2nd Customer
1st Customer
a1,(1,1)
Distance
Time
e(1,1)
w 1,(1,2)
t(1,0), (1,1)
s1,(1,1)
(1,0)Depot
(1,3)
ServiceTime
y1,(1,3)
WaitingTime
Delay Time
Travel Time
(1,2)
(1,1)
Depot
(1,0) e(1,2) e(1,3)l(1,1) l(1,2)l(1,3)a1,(1,2) a1,(1,3) a1,(1,0)L
d1,(1,1) d1,(1,2) d1,(1,3)
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Metaheuristics
• Simulated Annealing (SA)
• Tabu Search (TS)
• Genetic Algorithms (GA)
have been successfully applied to VRPTW…
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Tabu Search
• An intelligent problem solving technique based on flexible memory
• Neighbourhoods examined for new solutions some moves are tabu or forbidden
• Need to:– Define search history– Determine how to generate neighbourhood
solutions
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Definitions
• A neighbourhood– set of solutions formed from current
solution using a simple operation
• Tabu list– set of moves that are not allowed to avoid
repetition
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General Procedure
(i) determine initial solution, this become the current solution
(ii) if stopping criteria is not satisfied, generate neighbourhood solutions from the current solution, else finish
(iii) current solution is selected from non-tabu neighbourhood solutions found in
(ii), goto (ii)
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Neighbourhood Generation Techniques
• Adjacency Exchange– Adjacent links for a tour are exchanged
• Insert Exchange– Tour links are randomly exchanged (2
usually)• Cross Exchange
– Segments of tours (multiple customers) are exchanged
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Vehicle 1 Vehicle 2 Vehicle 1 Vehicle 2
Move
Move Operation
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Vehicle 1 Vehicle 2 Vehicle 1 Vehicle 2
Exchange
Exchange Operation
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Rule Determination
• Tabu Restrictions– Ban moves previously made– Can be conditional upon improvement
gained (aspiration criteria) • Selection Criteria
– Usually best neighbourhood solution is selected (even if no improvement gained)
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Neigbourhood Example
Random Swap Tabu Search Example (Retail Customers)Route Cost ($)
Current Solution 0 5 9 1 8 12 11 3 6 4 10 7 2 0 430.46( 5,11) 0 11 9 1 8 12 5 3 6 4 10 7 2 0 525.25( 2, 5) * 0 2 9 1 8 12 11 3 6 4 10 7 5 0 422.95( 5, 9) 0 9 5 1 8 12 11 3 6 4 10 7 2 0 439.67( 8, 9) 0 5 8 1 9 12 11 3 6 4 10 7 2 0 456.96( 5, 4) 0 4 9 1 8 12 11 3 6 5 10 7 2 0 469.03(11,12) 0 5 9 1 8 11 12 3 6 4 10 7 2 0 435.64( 4,12) 0 5 9 1 8 4 11 3 6 12 10 7 2 0 468.34(12, 6) 0 5 9 1 8 6 11 3 12 4 10 7 2 0 442.43(12,10) 0 5 9 1 8 10 11 3 6 4 12 7 2 0 471.02( 8, 6) 0 5 9 1 6 12 11 3 8 4 10 7 2 0 432.43
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Benefits of considering travel time variability in vehicle routing with time windows
0
2
4
6
8
10
12
14
16
-40 -30 -20 -10 0 10 20 30 40
Change in Mean Travel Speed (%)
Cost Savings (%)
Risk and Urban Distribution
2 International Patients registered…
Risk of delays modelled using stochastic programming & robust optimisation
Formed the basis for City Logistics modelling and intelligent transport systems research programs
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Collaborative Distribution
• Shared storage location(s)• Networks restructured using advanced
vehicle routing & scheduling systems• Distribution to outlets by areas & priority• Substantial savings in transport costs (20-
30%)• Significant reduction in environmental &
social costs
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Product Swaps
Distribute 500kg between each site
Vehicle capacity = 2000kg
Each site ≥ 1 vehicle
Transhipment possible at each site
Based on distributing electrical goods between retail shops in Melbourne
Concept could be applied to multiple carriers, horizontal collaboration (Fischer et al, 1995)
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2 3
1 5
4
4 5 vehicles no transhipment
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4 4 vehicles pickups at store w/o vehicle
2 3
1 5
4
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4 vehicles with transhipment at stores
2 3
15
4
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4
2 3
1 5
4
4 4 vehicles with transhipment at common location
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Network Analysis
0
100
200
300
400
500
600
5 veh. 4 veh. Good 4 veh. Opt 4 veh. Trans
Configuation
Dis
tance T
ravelle
d (km
)
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Collaborative Distribution in Melbourne
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Independent Networks from suppliers
1
2
3
4
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Collaborative Network
Around 20% saving in distance travelled
4
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B2C Food Items
DensityHousehold
Characteristics
Area
Population
M arket Share Custom ers
T rip Frequency
Load
Hom e Deliveries Shop Sales
T im e W indow sDistanceT ravelled
VRS Deliveries
Delivery F leetCharacteristics
D istribution F leetCharacteristics
VRS D istribution
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Single Urban DC
stores
m ediumdensity
low density
DC
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Regional DC’s
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VKT with 1 DC
Internet Sales0% 5% 10%
Suppliers to distribution centre 481.1 481.1 481.1Distribution to stores 458.5 458.5 426.9Stores and homes (customers) 24037 22835.3 21633Deliveries to homes 0 2805.9 6346.2Total 24977 26580.9 28888Increase (%) 6.4 15.7
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VKT with regional DCs
Internet Sales0% 5% 10%
Suppliers to distribution centre 481.1 481.1 481.1Distribution to stores 458.5 458.5 426.9Stores and homes (customers) 24037 22835.3 21633Deliveries to homes from RDC’s 0 1077.1 1841.5Distribution to RDC’s from MDC 0 214.8 214.8Total 24977 25066.8 24598Change (%) 0.4 -1.5
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E-commerce supermarket home delivery network
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Extended length, 36 Mail Cages Rigid
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Standard 12.5m, 3AR 23T vs High Productivity 4AR 14.85m 28T PBS Vehicle
+37% productivity
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Depot Transfer Operation
Depot 1(LF=95%)
Depot 3(LF=90%)
Depot 2(LF=90%)
Depot 4
(LF=87%)
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Multi Drop Operation
Depot Customer 1 Customer 2 Customer 3 Customer 4
Customer N
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Domestic Postal Fleet Impacts
• Estimated Kilometre reduction 29%• Average Load Productivity increase 37%• Cost reduction -8% Rigid truck numbers -20%
(over 7 years) in Urban areas• Generated high interest and has attracted a
government and Industry scholarships
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Ongoing Research
• Exact solution procedures• Pickup & Delivery with transfers• Intermodal networks (road & rail)• Flexible trailer combinations• Combining VRS with simulation (agent based
modelling)• Incorporating travel time information (dynamic
routes)
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References
Hassall, K. and R.G. Thompson, (2011). Estimating the Benefits of Performance Based Standards Vehicles, Transportation Research Record, No. 2224, Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C., 94-101.
Taniguchi, E. and R.G. Thompson (2003). Predicting the effects of city logistics schemes, Transport Reviews, Vol. 23, No. 4, 489-515.
Taniguchi, E. and R.G. Thompson (2002). Modeling City Logistics, Transportation Research Record, No. 1790, Transportation Research Board, National Research Council, Washington DC, 45-51.
Taniguchi, E., R.G. Thompson, T. Yamada and R. Van Duin, (2001). City Logistics – Network Modelling and Intelligent Transport Systems, Elsevier, Pergamon, Oxford, 260pp.
Taniguchi, E., Thompson, R.G. Yamada, T. (2012) ‘Emerging techniques for enhancing the practical application of city logistics models’, Procedia - Social and Behavioral Sciences, vol. 39, pp. 3-18.
Thompson, R.G. and R. van Duin (2002). Vehicle Routing and Scheduling, in Innovations in Freight Transport, (E. Taniguchi and R.G. Thompson, Eds.), 47-64, WIT Press, Southampton.
Thompson, R.G. and Hassall, K.P. (2012), A collaborative urban distribution network, Procedia - Social and Behavioral Sciences, vol. 39, pp. 230-240.
Thompson, R.G., E. Taniguchi and T. Yamada, (2011). Estimating Benefits of Considering Travel Time Variability in Urban Distribution, Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C., No. 2238, 86-96.
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